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English(EN) Why does a seemingly mundane task for an LLM to perform like improving E2E test coverage consistently consume all my tokens on 3 different models 2 of them bein

LLM在测试覆盖率任务上面临token限制

作者 PulseAugur 编辑部 · [1 个来源] ·

一位Mastodon用户在尝试改进端到端测试覆盖率时，遇到了大型语言模型（LLM）耗尽其所有token的问题。他们指出，这个看似简单的任务在三种不同的模型（包括前沿模型）上都显示出资源消耗大的问题。当将工作分配给多个代理时，问题会加剧，导致处理时间更长。 AI

影响 凸显了当前LLM在实际开发任务中可能存在的效率低下和token消耗问题。

排序理由 用户对LLM性能限制的评论。

在 Mastodon — mastodon.social 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM在测试覆盖率任务上面临token限制

本文如何被排名

Signal score
4 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
用户对LLM性能限制的评论。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · mirv ·

    为什么像改进 E2E 测试覆盖率这样对 LLM 来说看似微不足道的任务，却总是耗尽我三种不同模型的所有 token，其中两种是

    Why does a seemingly mundane task for an LLM to perform like improving E2E test coverage consistently consume all my tokens on 3 different models 2 of them being frontier. Splliting up the work across agents makes it take even longer. Anyone have any tips? # ai # llm

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